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基于MaxEnt模型的中华枸杞潜在分布预测及其重要影响因子分析

唐燕 赵儒楠 任钢 曹福亮 祝遵凌

唐燕, 赵儒楠, 任钢, 曹福亮, 祝遵凌. 基于MaxEnt模型的中华枸杞潜在分布预测及其重要影响因子分析[J]. 北京林业大学学报, 2021, 43(6): 23-32. doi: 10.12171/j.1000-1522.20200103
引用本文: 唐燕, 赵儒楠, 任钢, 曹福亮, 祝遵凌. 基于MaxEnt模型的中华枸杞潜在分布预测及其重要影响因子分析[J]. 北京林业大学学报, 2021, 43(6): 23-32. doi: 10.12171/j.1000-1522.20200103
Tang Yan, Zhao Runan, Ren Gang, Cao Fuliang, Zhu Zunling. Prediction of potential distribution of Lycium chinense based on MaxEnt model and analysis of its important influencing factors[J]. Journal of Beijing Forestry University, 2021, 43(6): 23-32. doi: 10.12171/j.1000-1522.20200103
Citation: Tang Yan, Zhao Runan, Ren Gang, Cao Fuliang, Zhu Zunling. Prediction of potential distribution of Lycium chinense based on MaxEnt model and analysis of its important influencing factors[J]. Journal of Beijing Forestry University, 2021, 43(6): 23-32. doi: 10.12171/j.1000-1522.20200103

基于MaxEnt模型的中华枸杞潜在分布预测及其重要影响因子分析

doi: 10.12171/j.1000-1522.20200103
基金项目: 青海海西科技创新项目(枸杞育种和培育关键技术研究),国家自然科学基金项目(31770752)
详细信息
    作者简介:

    唐燕。主要研究方向:园林植物栽培与应用。Email:tangyan@njfu.edu.cn 地址:210037江苏省南京市玄武区龙蟠路159号南京林业大学风景园林学院

    责任作者:

    祝遵凌,博士,教授。主要研究方向:园林植物栽培与应用。Email:zhuzunling@aliyun.com 地址:同上

  • 中图分类号: S793.9;Q948.5

Prediction of potential distribution of Lycium chinense based on MaxEnt model and analysis of its important influencing factors

  • 摘要:   目的  中华枸杞在我国大多地区处于野生状态,应用方式较为局限。在全球气候变化背景下,预测中华枸杞当前及未来适宜分布区对其种质资源保护、合理引种栽培以及大面积推广具有重要意义。  方法  本研究基于ArcGIS与MaxEnt模型,利用124条中华枸杞分布数据和8个气候变量,评估了制约其地理分布的主要气候因子,并探讨了其在当前和未来的潜在分布区。  结果  当前中华枸杞的总适生区面积约占国土面积的36.73%,水平分布区间约为18° ~ 45°N、90° ~ 123°E。其中核心适宜区主要位于秦岭、太行山以及华东地区的浙江、江苏、安徽;西南地区的云南、贵州、重庆、四川;西北地区的甘肃、宁夏及其各自的邻近区域。影响其分布的主要气候因子为最冷月最低温度、最湿月降水量、最干月降水量、最暖月最高温度。随着未来气候变暖,中华枸杞总适生区面积与当前相比相差不大,但其主要核心适宜区有向“高海拔地区扩张”、“沿海地区迁移”及“中部聚集”的趋势,具体表现为:在陕西、甘肃等秦岭地区向高海拔地区扩张,东部沿海地区逐渐形成狭条带状核心适宜区,中部湖南、湖北等地区逐渐形成大面积片状核心适宜区。  结论  中华枸杞的适生区主要呈现广域和连续特点,而核心适宜区呈狭域、间断分布。影响其地理分布的气候因子主要为温度和降雨,其中最冷月最低温度是限制其分布的最主要气候因子。未来,我国东部沿海地区, 大别山地区,中部秦岭、大巴山等地区, 西南地区的四川、重庆等地及其邻近区域适合进行枸杞的引种、栽培及推广。

     

  • 图  1  MaxEnt模型中ROC预测结果

    Figure  1.  Prediction results with ROC in MaxEnt model

    图  2  当前气候条件下中华枸杞在我国的潜在分布区

    JL. 吉林;NMG. 内蒙古;LN. 辽宁;BJ. 北京;TJ. 天津;HB. 河北;GS. 甘肃;NX. 宁夏;QH. 青海;XJ. 新疆;SaX. 陕西;SX. 山西;SD. 山东;HN. 河南;SC. 四川;CQ. 重庆;HB. 湖北;AH. 安徽;JS. 江苏;ZJ. 浙江;JX. 江西;XZ. 西藏;GZ. 贵州;YN. 云南;HN. 湖南;GX. 广西;GD. 广东;FJ. 福建;TW台湾;HN. 海南。JL, Jilin; NMG, Inner Mongolia; LN, Liaoning; BJ, Beijing; TJ, Tianjin; HB, Hebei; GS, Gansu; NX, Ningxia; QH, Qinghai; XJ, Xinjiang; SaX, Shaanxi; SX, Shanxi; SD, Shandong; HN, Henan; SC, Sichuan; CQ, Chongqing; HB, Hubei; AH, Anhui; JS, Jiangsu; ZJ, Zhejiang; JX, Jiangxi; XZ, Tibet; GZ, Guizhou; YN, Yunnan; HN, Hunan; GX, Guangxi; GD, Guangdong; FJ, Fujian; TW, Taiwan; HN, Hainan.

    Figure  2.  Potential distribution of L. chinense in China under current climatic conditions

    图  3  主要气候因子的响应曲线

    Figure  3.  Response curves of major climate factors

    图  4  不同气候情景下中华枸杞潜在分布区

    Figure  4.  Potential distribution of L. chinense under different climate scenarios

    表  1  环境变量及其对MaxEnt 预测模型的相对贡献率

    Table  1.   Environmental variables and their relative contributions to the MaxEnt model

    变量编号
    Variable No.
    环境变量
    Environmental variable
    贡献率
    Contribution rate/%
    bio6 最冷月最低温度
    Min. temperature of the coldest month
    58.4
    bio13 最湿月降水量
    Precipitation of the wettest month
    27.2
    bio14 最干月降水量
    Precipitation of the driest month
    5.3
    bio5 最暖月最高温度
    Max. temperature of the warmest month
    4.7
    bio7 年均温变化范围
    Variation range of annual mean temperature
    1.5
    bio2 昼夜温差月均值
    Monthly mean temperature difference between day and night
    1.1
    bio15 降水量变异系数
    Variation coefficient of precipitation
    1.1
    bio3 昼夜温差与年温差比值
    Ratio of diurnal temperature difference to annual temperature difference
    0.6
    下载: 导出CSV

    表  2  未来气候变化情景下环境变量对MaxEnt预测模型的相对贡献率

    Table  2.   Relative contributions of environmental variables to MaxEnt model under future climate change scenarios

    气候变化情景
    Climate change scenario
    环境变量及其贡献率 Environmental variable and its contribution rate/%
    bio6bio13bio14bio5bio7bio2bio15bio3
    RCP2.6Ⅰ 51.5 32.6 5.6 4.9 0.9 2.5 0.9 1.2
    RCP8.5Ⅰ 55.8 29.0 4.7 5.4 0.4 1.8 0.7 2.2
    RCP2.6Ⅱ 53.7 31.7 4.3 5.2 0.8 1.3 2.2 0.9
    RCP8.5Ⅱ 52.9 31.6 4.5 5.8 1.7 0.6 1.5 1.4
    注:Ⅰ. 2050s;Ⅱ. 2070s。下同。Notes: Ⅰ, 2050s; Ⅱ, 2070s. Same as below.
    下载: 导出CSV

    表  3  气候变化情景下中华枸杞不同等级适生区面积及其百分比

    Table  3.   Areas and percentages of suitable areas for different levels of L. chinense in climate change scenarios

    气候变化情景
    Climate change scenario
    百分比 Percentage/%面积 Area/104 km2
    核心适宜区
    Core suitable area
    一般适宜区
    General suitable area
    不适宜区
    Unsuitable area
    核心适宜区
    Core suitable area
    一般适宜区
    General suitable area
    不适宜区
    Unsuitable area
    当前 Current 8.76 27.97 63.27 84.21 268.88 608.24
     RCP2.6Ⅰ 7.34 31.09 61.57 70.50 298.79 591.72
     RCP8.5Ⅰ 7.29 29.59 63.12 70.01 284.38 606.62
    平均 Average 7.31 30.34 62.35 70.25 291.58 599.17
     RCP2.6Ⅱ 9.30 27.66 63.04 89.38 265.83 605.80
     RCP8.5Ⅱ 8.64 27.43 63.93 83.07 263.57 614.36
    平均 Average 8.97 27.54 63.48 86.23 264.70 610.08
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-04-09
  • 修回日期:  2020-05-22
  • 网络出版日期:  2021-06-03
  • 刊出日期:  2021-06-30

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